1. Field of the Invention
The present invention teaches a novel hybrid-dual-Fourier inverse algorithm for fast three dimensional (3D) tomographic image reconstruction of objects in highly scattering media using measured data from multiple sources and multiple detectors. This algorithm can be used for noninvasive screening, detection, and diagnosis of cancerous breast and prostate lesions, and for locating hidden objects in high scattering media, such as planes, tanks, objects within an animal or human body, or through cloud, fog, or smoke, mines under turbid water, and corrosion under paint.
2. Description of the Related Art
Turbid media blur and make objects inside difficult to be detected. Light propagation is diffusive, making the object inside not detectable using conventional imaging methods. In highly scattering media, when the object is located inside a turbid medium with depth greater than 10 scattering length, the object cannot be seen easily using ballistic light. It is also difficult to use time-gated transillumination technique to image objects in turbid media with L/Is>20, with L the size of the medium and Is the scattering length, because of photon starvation of the ballistic light. To overcome this difficulty one uses the image reconstruction of light in the medium to get three-dimensional image. This requires understanding of how light travels in the turbid media, and an appropriate inverse algorithm. Objects, such as tumors, aircraft, and corrosion in highly scattering media can be imaged using the novel image algorithm in turbid media. Turbid media include human tissue, cloud, and under paint.
Early detection and diagnosis of breast and prostate cancers is essential for effective treatment. X-ray mammography, the modality commonly used for breast cancer screening, cannot distinguish between malignant and benign tumors, and is less effective for younger women with dense fibrous breasts. If a tumor is suspected from a x-ray mammogram, a biopsy that requires invasive removal of tissue from the suspect region need be performed to determine if the tumor is benign or malignant. In a majority of the cases, the biopsy turns out to be negative, meaning the tumor is benign. Besides being subject to an invasive procedure, one has to wait an agonizing period until the biopsy results are known. A breast cancer screening modality that does not require tissue removal, and can provide diagnostic information is much desired.
Prostate cancer has a high incidence of mortality for men. Every year, nearly 180,000 new prostate cancer cases are diagnosed, and prostate cancers in U.S annually cause about 37,000 deaths. The developed cancers may spread to the lymph nodes or bones causing persistent and increasing pain, abnormal function, and death. The detection and treatment of early small prostate cancers are most important to prevent death attributable to prostate cancer. Current noninvasive approaches to detect the early prostate include the ultrasound, MRI and CT imaging, which have poor spatial resolution and contrast. Other means, such as needle biopsy, are invasive. Optical image as disclosed here can be used to image tumors in prostate.
Optical tomography is being developed as a noninvasive method that uses nonionizing near-infrared (NIR) light in the 700–1500 nm range to obtain images of the interior of the breast and prostate. Tissues scatter light strongly, so a direct shadow image of any tumor is generally blurred by scattered light. A technique, known as inverse image reconstruction (IIR), may help circumvent the problem of scattering. An IIR approach uses the knowledge of the characteristics of input light, measured distribution of light intensity that emerges from the illuminated breast and prostate, and a theoretical model that describes how light propagates through breast and prostate to construct an image of the interior of breast and prostate. Back scattering and transmission geometries are used for breast. For prostate, backscattering geometry is more suitable. The image reconstruction methods can also be used to image objects in hostile environments of smoke, cloud, fog, ocean, sea, and to locate corrosion under paint.
Although the problem has received much attention lately, that development of optical tomography has been slow. One of main difficulties is lack of an adequate algorithm for inversion image reconstruction, which is able to provide a 3D image in reasonable computing times. Recent algorithms and methods have been developed to solve the inverse problem in order to produce images of inhomogeneous medium, including finite-element solutions of the diffusion equation and iterative reconstruction techniques, modeling fitting, least-square-based and wavelet based conjugate-gradient-decent methods. Examples of references which disclose this technique include: H. B. Jiang et al, “Frequency-domain optical image reconstruction in turbid media: an experimental study of single-target detectability,” Appl. Opt. Vol. 36 52–63 (1997); H. B. O'Leary et al, “Experimental image of heterogeneous turbid media by frequency-domain diffusing-photon tomography,” Opt. Lett. Vol. 20, 426–428 (1995); S. Fantini et al, “Assessment of the size, position, and optical properties of breast tumors in vivo by noninvasive optical methods,” Appl. Opt. Vol 36, 170–179 (1997); W. Zhu et al “Iterative total least-squares image reconstruction algorithm for optical tomography by the conjugate gradient method,” J. Opt. Soc. Am. A Vol. 14 799–807 (1997), all of which are incorporated herein by reference. These methods take significantly long computing time for obtaining a 3D image to be of use in clinical applications and other detections. These methods require a linearly or non-linearly inverse of a group of equations, which have huge unknown augments that equal to the number of voxels in a 3D volume (a voxel is a small volume unit in 3D volume, and corresponds to a pixel in 2D plane).
Using a Fourier transform inverse procedure can greatly reduce computing time. Examples of references that disclose this technique include: X. D. Li et al, “Diffraction tomography for biomedical imaging with diffuse-photon density waves,” Opt. Lett. Vol. 22, 573–575 (1997); C. L. Matson et al, “Analysis of the forward problem with diffuse photon density waves in turbid media by use of a diffraction tomography model,” J. Opt. Soc. Am. A Vol. 16, 455–466 (1999); C. L. Matson et al, “Backpropagation in turbid media,”, J. Opt. Soc. Am. A Vol. 16, 1254–1265 (1999), all of which are incorporated herein by reference. In the Fourier transform procedure, the experimental setup should satisfy the requirement of spatial translation invariance, which restricts, up to now, use of a single laser source (a point source or a uniformly distributed plane source) with a 2D plane of detectors in parallel (transmission or reflection) geometry. This type of experimental setup can acquire only a set of 2D data for continuous wave (CW) or frequency-domain tomography, which is generally not enough for reconstruction of a 3D image, resulting in uncertainty in the depth of the objects in 3D image.
To overcome this difficulty of lack of enough data for 3D image in tomography using a Fourier procedure, we have in the past developed algorithms to acquire time-resolved optical signals, which provides an additional 1D (at different times) of acquired data, so 3D image reconstruction can be performed. Examples of references which disclose this technique include: R. R. Alfano et al: “Time-resolved diffusion tomographic 2D and 3D imaging in highly scattering turbid media,” U.S. Pat. No. 5,931,789, issued Aug. 3, 1999; U.S. Pat. No. 6,108,576, issued Aug. 22, 2000; W. Cai et al: “Optical tomographic image reconstruction from ultrafast time-sliced transmission measurements,” Appl. Optics Vol. 38, 4237–4246 (1999); M. Xu et al, “Time-resolved Fourier optical diffuse tomography”, JOSA A Vol. 18 1535–1542 (2001). Schotland and Markel developed inverse inversion algorithms using diffusion tomography based on the analytical form of the Green's function of frequency-domain diffusive waves, and point-like absorbers and scatterers. Examples of references which disclose this technique include: V. A. Markel, J. C. Schotland, “Inverse problem in optical diffusion tomography. I Fourier-Laplace inversion formulas”, J. Opt. Soc. Am. A Vol. 18, 1336 (2001); V. A. Markel, J. C. Schotland, “Inverse scattering for the diffusion equation with general boundary conditions”, Phys. Rev. E Vol. 64, 035601 (2001), all of which are incorporated herein by reference.
From the viewpoint of data acquisition in parallel geometry, however, it is desirable to use a 2D array of laser sources, which can be formed by scanning a laser source through a 2D plane, and a 2D plane of detectors, such as a CCD camera or a CMOS camera. Each illumination of laser source produces a set of 2D data on the received detectors. For CW or frequency-modulated laser source, this arrangement can produce a set of (2D, 2D)=4D data in a relatively short acquisition time, with enough accuracy and at reasonable cost. When time-resolved technique is applied using a pulse laser source, a set of 5D data can be acquired. In these cases the inverse problem of 3D imaging is over-determined, rather than under-determined for the case of using a single CW or frequency domain sources, and thus produces a much more accurate 3D image.
The key point is how to develop an algorithm, which is scientifically proper, and runs fast enough to produce a 3D image, so it can be realized for practical clinical applications and other field applications.